Headquartered in Detroit, Michigan, Urban Science is a global automotive consultant and technology provider that delivers data-driven solutions for original equipment manufacturers and dealerships. Since its founding in 1977, the company has utilized and leveraged data science, analytics, and software to help automotive brands optimize performance and make smarter market decisions.

Dealerships and parts manufacturers often face challenges ingesting and interpreting sales data and transforming it into actionable steps. Predicting key sales metrics, individual dealership anomalies and per-part demand is very challenging but important to price parts effectively.

Our Optimizing OEM Parts Sales Using AI-Powered Analytics project delivers AI-driven executive dashboards and prioritized visual summaries that highlight performance trends, operational risks, growth opportunities, and inventory gaps across regions. AI-generated insights interpret key changes in demand, inventory and pricing, translating complex data into clear, actionable recommendations that support faster and more confident decisions.

Instead of relying on intuition to guide demand and inventory decisions, clients are presented personalized bundling opportunities based on individual and regional sales performance. Price history and predicted trends are viewable at a glance on the predictions page. Dealerships experience less backorder and dead stock based on customized, data-driven inventory optimization recommendations.

The system uses a client-server architecture with an Angular front end and a .NET web API back end connected to a Microsoft Azure SQL database. Predictive models run in Azure Machine Learning, AI-driven insights are generated using Azure OpenAI and an Azure AI Foundry agent. These services produce explainable recommendations from sales, inventory and pricing data.